Extracting Image Texture Features Using Gabor Wavelets

This document presents an approach for extracting image texture features using Gabor wavelets, implemented in MATLAB programming language. Gabor wavelets function as frequency and orientation-based filters that effectively capture texture information from images. The implementation involves creating a bank of Gabor filters with varying orientations and frequencies, typically achieved through MATLAB's gaborFilterBank function or custom implementations using sinusoidal gratings modulated by Gaussian envelopes. When applied to an input image, these filters produce a set of response images, where each response corresponds to texture features at specific orientations and frequencies. The computational process typically involves 2D convolution operations between the image and each Gabor filter kernel. Following filter application, further processing and analysis of the response images enable extraction of meaningful texture information, often involving statistical measures like mean, variance, or energy calculations from the filtered responses. Gabor wavelet-based texture feature extraction remains a widely adopted methodology, particularly in computer vision and image processing applications, due to its biological relevance and effectiveness in representing texture patterns.